Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells
- Korea Advanced Institute of Science and Technology, Republic of Korea
- Tomocube Inc, Republic of Korea
Abstract
The immunological synapse (IS) is a cell-cell junction between a T cell and a professional antigen-presenting cell. Since the IS formation is a critical step for the initiation of an antigen-specific immune response, various live-cell imaging techniques, most of which rely on fluorescence microscopy, have been used to study the dynamics of IS. However, the inherent limitations associated with the fluorescence-based imaging, such as photo-bleaching and photo-toxicity, prevent the long-term assessment of dynamic changes of IS with high frequency. Here, we propose and experimentally validate a label-free, volumetric, and automated assessment method for IS dynamics using a combinational approach of optical diffraction tomography and deep learning-based segmentation. The proposed method enables an automatic and quantitative spatiotemporal analysis of IS kinetics of morphological and biochemical parameters associated with IS dynamics, providing a new option for immunological research.
Data availability
We have provided pre-processing and post-processing codes, and training and validation datasets used in Figure 3-Video 1 (https://osf.io/9w32p/). Also, the Unet architecture code is available in https://github.com/JinyeopSong/190124_CART-Segmentation-best.
Article and author information
Author details
Funding
National Research Foundation of Korea (2017M3C1A3013923)
- Moosung Lee
- Jinyeop Song
- Geon Kim
- YongKeun Park
National Research Foundation of Korea (2015R1A3A2066550)
- Moosung Lee
- Jinyeop Song
- Geon Kim
- YongKeun Park
National Research Foundation of Korea (2018K000396)
- Moosung Lee
- Jinyeop Song
- Geon Kim
- YongKeun Park
The Ministry of Science and ICT (2014M3A9D8032525)
- Young-Ho Lee
- Chan Hyuk Kim
The Ministry of Science and ICT (N11190028)
- Young-Ho Lee
- Chan Hyuk Kim
National Research Foundation of Korea (2019R1A2C1004129)
- Young-Ho Lee
- Chan Hyuk Kim
The funders had no role in study design, data collection, interpretation, or the decision to submit the work for publication.
Copyright
© 2020, Lee et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells
eLife 9:e49023.
https://doi.org/10.7554/eLife.49023
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